## Introduction to Operations Research, Volume 1-- This classic, field-defining text is the market leader in Operations Research -- and it's now updated and expanded to keep professionals a step ahead -- Features 25 new detailed, hands-on case studies added to the end of problem sections -- plus an expanded look at project planning and control with PERT/CPM -- A new, software-packed CD-ROM contains Excel files for examples in related chapters, numerous Excel templates, plus LINDO and LINGO files, along with MPL/CPLEX Software and MPL/CPLEX files, each showing worked-out examples |

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Page 408

The arcs of a network may have a

of trams on the roads of Seervada Park in Sec. 9.1. Table 9.1 gives several

examples of

...

The arcs of a network may have a

**flow**of some type through them, e.g., the**flow**of trams on the roads of Seervada Park in Sec. 9.1. Table 9.1 gives several

examples of

**flow**in typical networks. If**flow**through an arc is allowed in only one...

Page 422

Maximize the

water through a system of aqueducts. 5. Maximize the

transportation network. For some of these applications, the

network ...

Maximize the

**flow**of oil through a system of pipelines. 4. Maximize the**flow**ofwater through a system of aqueducts. 5. Maximize the

**flow**of vehicles through atransportation network. For some of these applications, the

**flow**through thenetwork ...

Page 429

Employing the equations given in the bottom right-hand corner of the figure,

these

nodes (see columns H and I). These net

transshipment ...

Employing the equations given in the bottom right-hand corner of the figure,

these

**flows**then are used to calculate the net**flow**generated at each of thenodes (see columns H and I). These net

**flows**are required to be 0 for thetransshipment ...

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activity additional algorithm alternative amount analysis apply assignment assumed basic variable begin BF solution calculate called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution customers decision demand described determine developed distribution entering equations estimated example expected feasible FIGURE final flow formulation given gives hour identify illustrate increase indicates initial inventory iteration linear programming machine Maximize mean million Minimize month needed node objective function obtained operations optimal optimal solution original parameter path payoff perform plant player possible presented Prob probability problem procedure profit programming problem queueing respectively resulting shown shows side simplex method solution solve step strategy Table tableau tion transportation unit waiting weeks